Insights

Professional baseball teams have long used metrics to measure player performance. The Earned Run Average (ERA) has been at the cornerstone of baseball metrics, since its inception in 1912. Traditionalists have abhorred the use of new metrics conceived by Sabrematricians, however, to many they have proven quite valuable.

In a recent paper to be published and presented at the Northeast Decision Sciences Institute Conference in Providence, RI in March, the team from 5 Element Analytics scrutinized the ERA statistic to help formulate a better approach to identifying pitching performances that would be candidates for exclusion, otherwise known in the statistical world as outliers.

There is a great deal of insight in this research that exposes the impact of these hidden outliers, which can really create a true measure of a pitchers performance-

Tyler Levine, Pitcher for The Long Island Ducks Professional Baseball Club

The new approach discusses how outliers can be identified using statistical methods by comparing the change in variance of a set of observations by the removal of a single observation. In this way, a single bad game, or even an unusually good game are identified thus allowing for a true measure of performance. The data was initially tested on the top 10 pitchers in the National League, and identified fewer outliers than other methods, but found a better representation of a pitcher’s “True” ERA.

Since earned runs follow a count process, identifying outliers have benefits in a number of different areas. The approach could have significant implications for use in other areas such as healthcare, and transportation.

We would like to thank Tyler Levine and Long Island Baseball, in Bellmore, NY for their support during this research.

Click Here for a copy of the paper , “Outier Identification of Count Data Using Variance Difference”, being presented in Providence, RI, in April 2018 .

Many of Sun Tzu’s writings have been adapted in a number of industries such as investing, real estate, and sales. However, the application of Sun Tzu to analytics is especially interesting and insightful because it plays such as significant role in successful analytics projects.

Sun Tzu said,

“There are five dangerous faults which may affect a general:

(1) Recklessness, which leads to destruction;

(2) cowardice, which leads to capture;

(3) a hasty temper, which can be provoked by insults;

(4) a delicacy of honor which is sensitive to shame;

(5) over-solicitude for his men, which exposes him to worry and trouble. “

How could one relate anything from analytics to these precepts? Very easily, if you are a student of strategy, and of Sun Tzu.

If an data analyst hurries their work or provides information in manner that is not complete, it leads to the the destruction of the work and possibly the destruction of companies or departments. An analysis that hastily attempts to identify a market segment, marketing campaign or competitive price comparison, can lead to disastrous results. This is not to say that speed isn’t critical, on the contrary as we will see in another article, speed is important, but reckless speed, kills.

Second, an analyst, must have the courage to state what must be said. They cannot hide the information or fear retribution from commanders and leaders. It is their responsibility to give their leadership critical, accurate and timely information whether its good or bad.

Third, analysts must leave the ego at the door. They cannot be tempted to lash out that their solution is the only reasonable one. While confidence and conviction are important, analysts must listen to all inputs in order to ensure that their solutions are not reckless. If there is a rush to judgement or just a defense of ones own position, the battle will be lost.

Fourth, if one is afraid to stand behind their decisions, the risk increases of hesitation and an analysis that is at best incomplete. Analysts must be willing to stand by their work, and be willing to be humble about their results, and not worry about preserving an undeserved honor. Honor and praise will come from decisiveness and completeness. However, a good analyst will shun overt and excessive praise because the work is never done.

Finally, our team is very important and data scientists are very difficult to come by; however, if leaders of analytic teams are worried about the sensitivities of their team or try and make things too comfortable, the analytics team will never be sharp for difficult projects. Training and goals are necessary for the analytics team to excel in what they do, comfort and complacency are the real enemy.

Sun Tzu’s effect on business spans many disciplines. Analytics teams are no exception. We can learn much from Sun Tzu and in our next articles, we will explore more of how to apply Sun Tzu to your data Science team.

In our company, we face the normal everyday pressures of businesses. We always work hard for our clients and are continually seeking ways to improve. We don’t let ourselves forget that we are humans as well. We get excited and happy when our ideas become reality and we get frustrated and stressed when we just can seem to find the answers. So how do we overcome periodic downturns of creativity? Continue reading The key to our success….→

Growth curves are a critical part of many different disciplines including the physical disciplines, however, many business, with the exclusion of financial companies, tend to neglect growth curves due to the preference of simpler, easy to implement linear lines. They are often misunderstood and when incorrectly applied, lead to very divergent results. If used properly, however, they are a great way to understand long term behavior of business activity. This article presents a brief analysis of a few different curves and compares them to a linear approach and further examines their application.

Analytics projects can be very complex and require an appropriate level of expertise. The various stages of an analytics project incude determination of goals, collection of data, cleansing of data, statistical analysis, and presentation of findings. According to Gartner over half of the analytics projects fail, which is determined as a project that fails to meet its stated objects and/or runs over budget or over the stated time.

We have identified the top three reasons why analytics projects fail and it might not be what you think.

1) Lack of a Senior Sponsor

Projects lacking sponsorship of senior leadership have the highest likelihood of failure since they aren’t given the requisite attention of other projects. Disconnected leadership fails to identify or recognize the valuable insights provided by well conducted analytics projects. Further, this dissonance resonates to analysts and data scientists who may not feel their work is valued or may not feel their efforts will be realized and thereby limit their efforts and scope of results. Senior sponsors should be C-level or high level decision makers in the strategic business units, and it is incumbent upon analytics leaders to obtain the necessary sponsorship and support for their teams.

2) Lack of a clear question to be answered

Its the age old question of what are we doing and why for starters. Analytics projects require the inquisitive nature of business units to fuel the analysis. Understanding the nature of the data, and having a clear objective, solidifies the work effort and creates a less amorphous outcome. The question should be business oriented and ask for answers critical to the business such as “Who are our most valuable customers?”, “Which customers are more likely to purchase the higher level product”, and “What happens if I increase/decrease my price and how will it affect long term customer retention”. When these types of questions are asked, analytics becomes a tool empowering business leaders.

3) Lack of reasonable action

“Action is the foundational key to all success”- Pablo Picasso

Your business units and senior leadership must be willing to take clear and decisive action based on the results provided by analytics projects. Failure to do so results in missed opportunities and complacency. “Let’s increase our price and analyze the results.”, “Let’s focus our latest marketing effort on the highest value customers”, and “Increase our communication on customers who may be ready to quit based on the results” are clear actions from results. The absence of these actions or indecisiveness becomes a clear indicator of a lack of trust in the data, analysis or analysts, and leads to failure.

Action should follow the questions from senior leadership, who are willing to trust the data and analysis as a compliment to the institutional knowledge of the team entrusted to execute on the directions. Analytics projects provide insights, they are not the end but rather a means to an end requiring buy-in and trust at all levels.